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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

Section 3.3. Research networks

In Europe, collaborations for multi-database studies have been strongly encouraged over the last years by the drug safety research funded by the European Commission (EC) and public-private partnerships such the Innovative Medicines Initiative. The funding resulted in the conduct of groundwork necessary to overcome the hurdles of data sharing across countries. In the US, the HMO Research Network, the Vaccine Safety Datalink (VSD) and Sentinel are examples of consortia involving health maintenance organisations that have formal, recognised research capabilities.

 

Networking implies collaboration between investigators in sharing expertise and resources. The ENCePP Database of Research Resources may facilitate such networking by providing an inventory of research centres and data sources that might collaborate on specific pharmacoepidemiology and pharmacovigilance studies in Europe. It allows the identification of centres and data sets by country, type of research and other relevant fields. In addition, an important component of collaboration among researchers is the potential for pooling of raw data and meta-analyses to maximise the information gathered for an issue that is addressed in different databases.

 

From a methodological point of view, data networks have many advantages:

  • By increasing the size of study populations, networks may shorten the time needed for obtaining the desired sample size. Hence, networks can facilitate research on rare events and accelerate investigation of drug safety issues.

  • Heterogeneity of treatment options across countries allows studying the effect of individual drugs.

  • Multidatabase studies may provide additional knowledge on whether a drug safety issue exists in several countries and thereby reveal causes of differential drug effects, on the generalisability of results, on the consistency of information and on the impact of biases on estimates.

  • Involvement of experts from various countries addressing case definitions, terminologies, coding in databases and research practices provides opportunities to increase consistency of results of observational studies.

  • Sharing of data sources facilitates harmonisation of data elaboration and transparency in analyses and benchmarking of data management.

Different models have been applied for combining data from various databases ranging from a very disparate to a more integrated approach:

These different models have different strengths and weaknesses and present different challenges. These may include:

  • Differences in the underlying health care systems and mechanisms of data generation and collection

  • Differences in culture and experience between academia, public institutions and private partners.

  • Different ethical and governance requirements in each country regarding processing of anonymised or pseudo-anonymised healthcare data.

  • Mapping of differing disease coding systems (for examples, the International Classification of Disease, 10th Revision (ICD-10), Read codes in the United Kingdom and the International Classification of Primary Care (ICPC-2)) and languages of narrative medical information.

  • Choice of data sharing model and access rights of partners.

  • Validation of diagnoses and access to source documents for validation.

  • Issues linked to intellectual property and authorship.

  • Sustainability and funding mechanisms.

Experience has shown that many of these difficulties can be overcome by full involvement and good communication between partners, and a project agreement between network members defining roles and responsibilities and addressing issues of intellectual property and authorship. Technical solutions also exist for data sharing and mapping of terminologies, such as those adopted in the EMIF project.

 

Multi-centre, multi-database studies with common protocols: lessons learnt from the IMI PROTECT project(Pharmacoepidemiol Drug Saf 2016;25(S1):156-165) concludes that conducting multi-database studies requires very detailed common protocols and data specifications that reduce variability in interpretations by researchers. Whilst a priori pooling data from several databases may disguise heterogeneity that may provide useful information on the safety issue under investigation, parallel analysis of databases allow exploring reasons for heterogeneity through extensive sensitivity analyses. This approach eventually increases consistency in findings from observational drug effect studies or reveal causes of differential drug effects.

Many pharmacoepidemiology research networks in the EU have been established under EC grant agreements. The coming years should demonstrate whether and how the expertise and infrastructures involved could be maintained and used in the conduct of post-authorisation studies.

 
Individual Chapters:

 

1. General aspects of study protocol

2. Research question

3. Approaches to data collection

3.1. Primary data collection

3.2. Secondary use of data

3.3. Research networks

3.4. Spontaneous report database

3.5. Using data from social media and electronic devices as a data source

3.5.1. General considerations

4. Study design and methods

4.1. General considerations

4.2. Challenges and lessons learned

4.2.1. Definition and validation of drug exposure, outcomes and covariates

4.2.1.1. Assessment of exposure

4.2.1.2. Assessment of outcomes

4.2.1.3. Assessment of covariates

4.2.1.4. Validation

4.2.2. Bias and confounding

4.2.2.1. Choice of exposure risk windows

4.2.2.2. Time-related bias

4.2.2.2.1. Immortal time bias

4.2.2.2.2. Other forms of time-related bias

4.2.2.3. Confounding by indication

4.2.2.4. Protopathic bias

4.2.2.5. Surveillance bias

4.2.2.6. Unmeasured confounding

4.2.3. Methods to handle bias and confounding

4.2.3.1. New-user designs

4.2.3.2. Case-only designs

4.2.3.3. Disease risk scores

4.2.3.4. Propensity scores

4.2.3.5. Instrumental variables

4.2.3.6. Prior event rate ratios

4.2.3.7. Handling time-dependent confounding in the analysis

4.2.4. Effect modification

4.3. Ecological analyses and case-population studies

4.4. Hybrid studies

4.4.1. Pragmatic trials

4.4.2. Large simple trials

4.4.3. Randomised database studies

4.5. Systematic review and meta-analysis

4.6. Signal detection methodology and application

5. The statistical analysis plan

5.1. General considerations

5.2. Statistical plan

5.3. Handling of missing data

6. Quality management

7. Communication

7.1. Principles of communication

7.2. Guidelines on communication of studies

8. Legal context

8.1. Ethical conduct, patient and data protection

8.2. Pharmacovigilance legislation

8.3. Reporting of adverse events/reactions

9. Specific topics

9.1. Comparative effectiveness research

9.1.1. Introduction

9.1.2. General aspects

9.1.3. Prominent issues in CER

9.1.3.1. Randomised clinical trials vs. observational studies

9.1.3.2. Use of electronic healthcare databases

9.1.3.3. Bias and confounding in observational CER

9.2. Vaccine safety and effectiveness

9.2.1. Vaccine safety

9.2.1.1. General aspects

9.2.1.2. Signal detection

9.2.1.3. Signal refinement

9.2.1.4. Hypothesis testing studies

9.2.1.5. Meta-analyses

9.2.1.6. Studies on vaccine safety in special populations

9.2.2. Vaccine effectiveness

9.2.2.1. Definitions

9.2.2.2. Traditional cohort and case-control studies

9.2.2.3. Screening method

9.2.2.4. Indirect cohort (Broome) method

9.2.2.5. Density case-control design

9.2.2.6. Test negative design

9.2.2.7. Case coverage design

9.2.2.8. Impact assessment

9.2.2.9. Methods to study waning immunity

9.3. Design and analysis of pharmacogenetic studies

9.3.1. Introduction

9.3.2. Identification of genetic variants

9.3.3. Study designs

9.3.4. Data collection

9.3.5. Data analysis

9.3.6. Reporting

9.3.7. Clinical practice guidelines

9.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes